Research and development of industrial data security based on adaptive differential privacy mechanisms

Qu Shengyu

doi:10.11959/j.issn.1000-0801.2026092

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Research and development of industrial data security based on adaptive differential privacy mechanisms

Research and Development | 更新时间：2026-04-08

- Research and development of industrial data security based on adaptive differential privacy mechanisms
- Telecommunications Science Vol. 42, Issue 3, Pages: 44-53(2026)
- 作者机构：
  
  中国工业互联网研究院（工业和信息化部密码应用研究中心），北京 100015
- 作者简介：
- 基金信息：
  
  Ministry of Industry and Information Technology (MIIT) 2021 Industrial Technology Fundamental Public Service Platform Project(2021-H026-1-1)
- DOI：10.11959/j.issn.1000-0801.2026092
  CLC： TP393
- Received：11 October 2025，
  
  Revised：2025-11-03，
  
  Accepted：18 November 2025，
  
  Published：20 March 2026
- 稿件说明：
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曲升宇.基于自适应差分隐私机制的工业数据安全研究与发展[J].电信科学,2026,42(03):44-53.

Qu Shengyu.Research and development of industrial data security based on adaptive differential privacy mechanisms[J].Telecommunications Science,2026,42(03):44-53.
曲升宇.基于自适应差分隐私机制的工业数据安全研究与发展[J].电信科学,2026,42(03):44-53. DOI： 10.11959/j.issn.1000-0801.2026092.

Qu Shengyu.Research and development of industrial data security based on adaptive differential privacy mechanisms[J].Telecommunications Science,2026,42(03):44-53. DOI： 10.11959/j.issn.1000-0801.2026092.

摘要

针对工业数据的高度敏感性、强关联性与实时性要求，提出自适应差分隐私（adaptive differential privacy，ADP）作为一种面向动态环境的隐私保护新范式。系统阐述ADP的理论演进与工业适配性，凝练出动态隐私预算调度、关联敏感度估计、隐私-效用均衡优化3条核心技术路径，并且结合工业控制系统、供应链协同、预测性维护三大典型场景验证其有效性。研究结果表明，ADP能够突破静态差分隐私的“效用-隐私”权衡困境，在复杂工业环境中实现隐私保护与数据价值的协同优化。未来，紧致隐私分析、高维异构数据处理、鲁棒性设计及标准化生态构建是工业数据安全的主要研究与应用方向。

Abstract

In response to the high sensitivity

strong correlation

and real‑time requirements of industrial data

an adaptive differential privacy (ADP) framework was proposed as a new paradigm for privacy protection in dynamic environments. The theoretical evolution and industrial adaptability of ADP were systematically elaborated. Three core technical pathways were distilled: dynamic privacy budget scheduling

correlation‑aware sensitivity estimation

and privacy‑utility balance optimization. The effectiveness of the approach was validated through three typical industrial scenarios: industrial control systems

supply‑chain collaboration

and predictive maintenance. The results demonstrate that ADP can overcome the “utility‑privacy” trade‑off inherent in static differential privacy

enabling synergistic optimization of privacy preservation and data value extraction in complex industrial settings. Finally

it was pointed out that compact privacy analysis

efficient processing of high‑dimensional heterogeneous data

robustness design

and the construction of a standardized ecosystem represented key future directions for research and application in industrial data security.

关键词

Keywords

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